Supporting Tutors in the Gig Economy with Automated Feedback: A Case Study on Ringle

2026-06-21

AI Summary

This study addresses the limitations of learner ratings as the primary source of instructor feedback on online education gig platforms, which often fail to support effective teaching improvement and quality assurance. Deploying an AI-driven automated feedback system for the first time on the Ringle platform, the authors generated structured insights by analyzing instructional content and evaluated instructor perceptions through a survey of 36 tutors. Findings reveal that while instructors generally held negative attitudes toward the automated feedback, they acknowledged its utility in self-monitoring and clarifying platform expectations, though they also reported confusion stemming from inconsistencies with learner-provided feedback. The work proposes design principles for AI feedback systems tailored to online education gig contexts, offering a novel pathway toward scalable, data-informed teaching quality governance.

Abstract

The rise of online tutoring platforms in the gig economy has made education more scalable, flexible, and on-demand. These platforms rely on learner evaluations as the primary feedback for tutors and platforms. However, such feedback offers limited guidance for tutors' improvement and makes it difficult to monitor tutor quality at scale. To this end, we explored AI-powered automated feedback and how tutors perceive and respond to it. We deployed a research probe on Ringle, a popular online English tutoring platform, that analyzed tutors' lessons and provided automated feedback. We then surveyed 36 tutors about their experience. Our findings reveal that while tutors perceived automated feedback more negatively than learner feedback, they found it useful for self-monitoring and understanding platform expectations, though discrepancies between them often caused confusion. Based on these insights, we propose design considerations for feedback systems for online educational gig platforms.

Problem

• online tutoring
• gig economy
• automated feedback
• tutor quality
• learner evaluation

Innovation

• automated feedback
• AI-powered tutoring
• gig economy
• online education
• tutor quality monitoring